https://www.mdu.se/

Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation-assisted machine learning model with two concepts: general (adding all predictors at once) and step-wise (adding predictors as they become available in sub-yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10-fold cross-validation. The model's performance on three data sets-a real-world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard-shows a respective R2$R<^>2$ of 0.90, 0.87, and 0.70. Step-wise inclusion of the predictors results differently for the real-world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real-world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates-except for the receiving yard-enhance the predictions. We aim to predict freight train delay departures from the yard by implementing a simulation-assisted machine learning model via two general and step-wise concepts for including the predictors. In the general concept, we use all the predictors from yard operations at once. In the step-wise concept, the predictors are added to the model based on the stages of the operation to understand how each predictor impacts the departure delay. Our machine learning model is trained by real-world and simulation data. image